Method and apparatus for obtaining vehicle loss assessment image, server and terminal device

ABSTRACT

Embodiments of the application provide a method, apparatus, server, and terminal device for obtaining a vehicle loss assessment image. A computer-implemented method for obtaining a vehicle loss assessment image comprises: receiving video data of a damaged vehicle; detecting one or more video images in the video data to identify a damaged portion in the one or more video images; classifying the one or more video images into one or more candidate image classification sets of the damaged portion based on the identified damaged portion; and selecting a vehicle loss assessment image from the one or more candidate image classification sets according to a screening condition.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of InternationalPatent Application No. PCT/CN2018/084760, filed on Apr. 27, 2018, whichis based on and claims priority to the Chinese Patent Application No.201710294010.4, filed on Apr. 28, 2017 and entitled “METHOD ANDAPPARATUS FOR OBTAINING VEHICLE LOSS ASSESSMENT IMAGE, SERVER ANDTERMINAL DEVICE.” The above-referenced applications are incorporatedherein by reference in their entirety.

TECHNICAL FIELD

This application relates to the field of computer image data processingtechnologies, and in particular, to a method, apparatus, server, andterminal device for obtaining a vehicle loss assessment image.

BACKGROUND

After a traffic accident of a vehicle occurs, if the vehicle makes aninsurance claim to its insurance company, the insurance company needsseveral loss assessment images to perform loss assessment andverification for the vehicle, and also to archive documents of theinsurance claim.

At present, vehicle loss assessment images are generally obtainedthrough photographing by an operator on the scene, and then vehicle lossassessment processing is performed according to the photographs taken onthe scene. The vehicle loss assessment images need to clearly reflectinformation of, e.g., a damaged portion, a damaged component, a damagetype, and a damage degree of a vehicle. Generally, a photographer isrequired to be acquainted with professional vehicle lossassessment-related knowledge to be able to photograph an imagesatisfying loss assessment processing requirements. This obviouslyrequires relatively high costs in manpower training and experienceaccumulation on loss assessment processing. In addition, there are somesituations in which a vehicle needs to be evacuated or moved as soon aspossible after a traffic accident, however it takes a relatively longtime for an insurance company operator to arrive at the scene of theaccident. Moreover, if a vehicle owner takes the initiative to takephotos or takes photos at a request of the insurance company operator toobtain some original loss assessment images, because the vehicle owneris not professional, the loss assessment images obtained by the vehicleowner often do not satisfy the loss assessment image processingrequirements. In addition, images photographed by the operator on thescene often need to be exported from a photographing device subsequentlyand manually screened to determine qualified loss assessment images.This also requires a relatively large amount of manpower and time,thereby reducing efficiency of obtaining the final loss assessmentimages required for loss assessment processing.

Existing manners of obtaining loss assessment images by an insurancecompany operator or a vehicle owner requires professional vehicle lossassessment-related knowledge. Manpower and time costs are relativelyhigh, and efficiency of obtaining loss assessment images satisfying theloss assessment processing requirements is relatively low.

SUMMARY

An objective of the specification is to provide a method, apparatus,server, and terminal device for obtaining a vehicle loss assessmentimage, to quickly generate high-quality loss assessment imagessatisfying loss assessment processing requirements through videorecording, performed by a photographer, of a damaged portion of adamaged vehicle, thereby improving loss assessment image obtainingefficiency and facilitating the operation of an operator.

A method for obtaining a vehicle loss assessment image may comprise:receiving video data of a damaged vehicle; detecting one or more videoimages in the video data to identify a damaged portion in the one ormore video images; classifying the one or more video images into one ormore candidate image classification sets of the damaged portion based onthe identified damaged portion; and selecting a vehicle loss assessmentimage from the one or more candidate image classification sets accordingto a screening condition.

In some embodiments, the one or more determined candidate imageclassification sets comprises: a close-up image set including one ormore video images displaying the damaged portion and a component imageset including one or more video images displaying a vehicle component towhich the damaged portion belongs.

In some embodiments, classifying one or more video images into theclose-up image set comprises: in response to determining that a ratio ofan area of the damaged portion to that of a video image including thedamaged portion is greater than a first preset ratio, classifying thevideo image into the close-up image set.

In some embodiments, classifying one or more video images into theclose-up image set comprises: in response to determining that a ratio ofa horizontal coordinate span of the damaged portion to a length of avideo image including the damaged portion is greater than a secondpreset ratio, and/or a ratio of a longitudinal coordinate span of thedamaged portion to a height of the video image including the damagedportion is greater than a third preset ratio, classifying the videoimage into the close-up image set.

In some embodiments, classifying one or more video images into theclose-up image set comprises: sorting video images including the damagedportion in a descending order of areas of the same damaged portion inthe video images; and selecting, from the sorted video images, first oneor more video images or one or more video images in each of which aratio of an area of the corresponding damaged portion to that of thevideo image is greater than a fourth preset ratio.

In some embodiments, the method may further comprise: in response todetecting that at least one of the close-up image set and the componentimage set of the damaged portion is empty, or the one or more videoimages in the close-up image set do not cover the entire damagedportion, generating a video recording prompt message; and sending thevideo recording prompt message to the terminal device.

In some embodiments, the method may further comprise: tracking thedamaged portion in the video data in real time to determine a region ofthe damaged portion in the video images; and in response to the damagedportion being out of a video image and subsequently re-entering a videoimage, tracking the damaged portion again to determine a new region ofthe damaged portion in the video image based on image feature data ofthe damaged portion.

In some embodiments, the method may further comprise: sendinginformation of the region of the tracked damaged portion to a terminaldevice for the terminal device to display the region of the damagedportion in real time.

In some embodiments, the method may further comprise: receiving newinformation of the damaged portion, wherein the new information of thedamaged portion is determined in response to the terminal device'schanging the region of the damaged portion based on a receivedinteractive instruction; and classifying the video images based on thenew information of the damaged portion.

In some embodiments, selecting a vehicle loss assessment image from theone or more candidate image classification sets according to a screeningcondition comprises: selecting at least one video image as a lossassessment image of the damaged portion from the one or more candidateimage classification sets according to clarity of the video images andfilming angles of the damaged portion in the video images.

In some embodiments, the method further comprise: in response todetecting that there are at least two damaged portions in the one ormore video images, determining whether a distance between the at leasttwo damaged portions satisfies a proximity condition; and in response todetermining that the distance between the at least two damaged portionssatisfies the proximity condition, simultaneously tracking the at leasttwo damaged portions, and obtaining loss assessment images of the atleast two damaged portions respectively.

An apparatus for obtaining a vehicle loss assessment image may comprise:one or more processors and one or more non-transitory computer-readablememories coupled to the one or more processors and configured withinstructions executable by the one or more processors to cause theapparatus to perform operations comprising: receiving video data of adamaged vehicle; detecting one or more video images in the video data toidentify a damaged portion in the one or more video images; classifyingthe one or more video images into one or more candidate imageclassification sets of the damaged portion based on the identifieddamaged portion; and selecting a vehicle loss assessment image from theone or more candidate image classification sets according to a screeningcondition.

A non-transitory computer-readable storage medium may be configured withinstructions executable by one or more processors to cause the one ormore processors to perform operations comprising: receiving video dataof a damaged vehicle; detecting one or more video images in the videodata to identify a damaged portion in the one or more video images;classifying the one or more video images into one or more candidateimage classification sets of the damaged portion based on the identifieddamaged portion; and selecting a vehicle loss assessment image from theone or more candidate image classification sets according to a screeningcondition.

The method, apparatus, and non-transitory computer-readable storagemedium for obtaining a vehicle loss assessment image provided in thespecification propose a solution of generating a video-based vehicleloss assessment image. A photographer may perform video recording of adamaged vehicle by using the terminal device, and captured video datamay be transmitted to the server of a system, and then the serveranalyzes the video data, identifies a damaged portion, and obtains,according to the damaged portion, different types of candidate imagesrequired for loss assessment. Then, a loss assessment image of thedamaged vehicle may be generated from the candidate images. According tothe implementations of this application, high-quality loss assessmentimages satisfying loss assessment processing requirements can be quicklyobtained, thereby improving efficiency of obtaining loss assessmentimages, and also reducing costs of obtaining and processing lossassessment images by insurance company operators.

BRIEF DESCRIPTION OF THE DRAWINGS

To describe the technical solutions in the embodiments of thespecification more clearly, accompanying drawings for describing theembodiments are briefly described below. Obviously, the accompanyingdrawings in the following description show merely some embodiments ofthe specification, and a person of ordinary skill in the art may stillderive other drawings from these accompanying drawings without creativeefforts.

FIG. 1 is a schematic flowchart of a method for obtaining a vehicle lossassessment image according to some embodiments of the specification;

FIG. 2 is a schematic structural diagram of a model for identifying adamaged portion in a video image that is constructed by using the methodaccording to some embodiments of the specification;

FIG. 3 is a schematic diagram of identifying a damaged portion by usinga damage detection model in the method according to some embodiments ofthe specification;

FIG. 4 is a schematic diagram of determining, based on an identifieddamaged portion, a close-up image according to some embodiments of thespecification;

FIG. 5 is a schematic structural diagram of a model for identifying adamaged component in a video image that is constructed by using themethod according to some embodiments of the specification;

FIG. 6 is a schematic diagram of a processing scenario of a method forobtaining a vehicle loss assessment image according to some embodimentsof the specification;

FIG. 7 is a schematic flowchart of the method for obtaining a vehicleloss assessment image according to other embodiments of thespecification;

FIG. 8 is a schematic flowchart of the method for obtaining a vehicleloss assessment image according to still other embodiments of thespecification;

FIG. 9 is a schematic flowchart of the method for obtaining a vehicleloss assessment image according to yet other embodiments of thespecification;

FIG. 10 is a schematic flowchart of the method for obtaining a vehicleloss assessment image according to further other embodiments of thespecification;

FIG. 11 is a schematic structural diagram of modules of an apparatus forobtaining a vehicle loss assessment image according to some embodimentsof the specification;

FIG. 12 is a schematic structural diagram of modules of anotherapparatus for obtaining a vehicle loss assessment image according tosome embodiments of the specification; and

FIG. 13 is a schematic structural diagram of a terminal device accordingto some embodiments of the specification.

DETAILED DESCRIPTION OF THE INVENTION

To make a person skilled in the art better understand the technicalsolutions of the specification, the technical solutions in theembodiments of the specification are further described below withreference to the accompanying drawings. Obviously, the describedembodiments are merely some rather than all of the embodiments of thespecification. All other embodiments obtained by a person of ordinaryskill in the art based on the embodiments of the specification withoutcreative efforts shall fall within the protection scope of theapplication.

FIG. 1 is a schematic flowchart of a method for obtaining a vehicle lossassessment image according to some embodiments of the specification. Thespecification provides method steps or apparatus modules shown in thefollowing embodiments or accompany drawings. In some embodiments,additional steps of the methods or additional modules of the apparatusesmay be included in the methods or the apparatuses without creativeefforts. In other embodiments, fewer steps or modules may be included inthe methods or the apparatuses without creative efforts. For example,some steps in a method or some modules in an apparatus may be integratedinto one step or one module. In the steps or modules in which nonecessary causal relationship logically exists, the execution order ofthe steps of a method or the connection among the modules of theapparatus is not limited to the execution orders or the connection shownin the embodiments or the accompany drawings of the specification. Whenthe steps or modules are applied to an apparatus, a server, or aterminal product, sequential execution or parallel execution may beperformed according to the steps or modules shown in the embodiments orthe accompany drawings (for example, in a parallel processing ormulti-thread processing environment, and even in environments includingdistributed processing and server clustering).

For clarity, the following embodiments are described by taking anembodiment scenario as an example, in which a photographer performsvideo recording by using a mobile terminal, and a server processescaptured video data to obtain one or more loss assessment images. Thephotographer may be an insurance company operator, and the photographerholds a mobile terminal to perform video recording of a damaged vehicle.The mobile terminal may be a mobile phone, a tablet computer, or any ofother general-purpose or dedicated devices having a video recordingfunction and a data communication function. A corresponding applicationmodule (for example, a vehicle loss assessment application (APP)installed on the mobile terminal) may be deployed on the mobile terminaland the server, to implement corresponding data processing. However, aperson skilled in the art can understand that the essential spirit ofthe solutions can be applied to other embodiment scenarios for obtainingvehicle loss assessment images. For example, the photographer may be avehicle owner, or after filming performed by using the mobile terminal,video data is processed and a loss assessment image is obtained on themobile terminal.

As shown in FIG. 1, the method for obtaining a vehicle loss assessmentimage according to some embodiments of the specification, may includethe following steps S1-S4.

S1: A client obtains captured video data, and sends the captured videodata to a server.

The client may include a general-purpose or dedicated device having avideo recording function and a data communication function, for example,a terminal device such as a mobile phone or a tablet computer. In otherexamples, the client may further include a computer device (such as a PCterminal) having a data communication function and a portable videorecording device connected to the computer device. A combination of thecomputer device and the portable video recording device is considered asa terminal device of the client in some embodiments. A photographerobtains the captured video data by using the client, and the capturedvideo data may be transmitted to the server. The server may include aprocessing device that analyzes and processes frames of images in thevideo data and determines loss assessment images. The server may includea logical unit apparatus having an image data processing and datacommunication function. From a perspective of data exchange, the serveris a second terminal device that performs data communication with theclient used as the first terminal device. Therefore, for ease ofdescription, a terminal on which the captured video data is obtainedthrough video recording of a vehicle is referred to as the client, and aterminal on which the loss assessment images are generated throughprocessing on the captured video data is referred to as the server. Inthe specification, it is not excluded that in some embodiments, theclient and the server are a same terminal device in which the client andthe server are physically connected.

In some embodiments of the specification, the video data obtainedthrough filming by using the client may be transmitted to the server inreal time, thereby facilitating rapid processing performed by theserver. In other embodiments, the video data may alternatively betransmitted to the server after the video recording performed by usingthe client is completed. If no network connection exists in the mobileterminal when being used by the photographer, the video recording may beperformed first, and the video data may be transmitted after the mobileterminal is connected to mobile cellular, a wireless local area network(WLAN), or a dedicated network. Certainly, even if the client canperform normal data communication with the server, the client mayalternatively asynchronously transmit the captured video data to theserver.

In some embodiments, the captured video data obtained by thephotographer by filming a damaged portion of the vehicle may include avideo segment, or multiple video segments, for example, multiplesegments of captured video data generated through multiple times offilming performed on a same damaged portion at different angles and indifferent distances, or captured video data of different damagedportions respectively obtained through filming of the damaged portions.Certainly, in some embodiments, complete filming may alternatively beperformed on the damaged portions of the damaged vehicle to obtain onevideo segment lasting a relatively long time.

S2: The server detects video images in the captured video data toidentify a damaged portion in the video images.

In some embodiments, the server may perform image detection on a videoimage in the captured video data, and identify and process the damagedportion of the vehicle in the video image. Generally, the identifieddamaged portion occupies a region in the video image and hascorresponding region information, for example, a location and a size ofthe region in which the damaged portion is located.

In some embodiments of detecting the damaged portion in the video image,the damaged portion in the video image may be identified by using aconstructed damage detection model. The damage detection model detectsthe damaged portion of the vehicle and the region of the damaged portionin the image by using a deep neural network. In an embodiment of thisapplication, the damage detection model may be constructed based on aconvolutional neural network (CNN) and a region proposal network (RPN)and in combination with a pooling layer, a fully-connected (FC) layer,and the like.

In some embodiments, the damage detection model used for identifying adamaged portion included in a video image may be pre-constructed byusing a designed machine learning algorithm. After the damage detectionmodel is trained based on samples, the damage detection model canidentify one or more damaged portions in the video image. The damagedetection model may be constructed by using a network model of the deepneural network or a varied network model of the deep neural networktrained based on samples. In some embodiments, the construction may bebased on the CNN and the RPN, in combination with the FC layer, thepooling layer, a data normalization layer, and the like. Certainly, inother embodiments, if the damaged portion is to be classified, aprobability output layer (Softmax) and the like may further be added tothe damage detection model. An example is shown in FIG. 2. FIG. 2 is aschematic structural diagram of a model for identifying a damagedportion in a video image that is constructed by using the methodaccording to some embodiments of the specification. FIG. 3 is aschematic diagram of identifying a damaged portion by using a damagedetection model in the method according to some embodiments of thespecification. The identified damaged portion may be displayed on theclient in real time.

The CNN is generally a neural network using a convolutional layer as amain structure and including others such as an activation layer, and ismainly used for image identification. In some embodiments, the deepneural network may be generated by using the convolutional layer andother important layers (such as sample damage images input to the modelfor training, the data normalization layer, and the activation layer),in combination with the RPN. In the CNN, a two-dimensional discreteconvolution operation in image processing is generally combined with anartificial neural network. This convolution operation may be used forautomatic feature extraction. The RPN may use a feature extracted froman image (arbitrary size) as input (which may be a two-dimensionalfeature extracted by using the CNN), and output a set of rectangulartarget proposal boxes. Each of the boxes has an object score.

In the foregoing embodiments, one or more damaged portions in the videoimage may be identified during model training. For example, duringtraining based on samples, a picture is input, and multiple regions ofthe picture may be output. If there is one damaged portion, a region ofthe picture may be output; if there are k damaged portions, k regions ofthe picture may be output; or if there is no damaged portion, zero imageregion is output. Selected parameters of the neural network may beobtained through mini-batch gradient descent training by using markeddata. For example, when a mini-batch equals 32, 32 training pictures areused as one input for training.

In other embodiments, the damage detection model may be multiple modelsand their variations based on the CNN and the RPN, such as Faster R-CNN,YOLO, and Mask-FCN. The CNN may use any CNN model, such as ResNet,Inception, VGG, or a variation thereof. Generally, the CNN part of theneural network may use a mature network structure, for example, anetwork such as Inception or ResNet, that achieves a relatively goodeffect in object recognition. For example, in a ResNet network, input isa picture, and output is multiple damaged regions and confidence (wherethe confidence herein is a parameter indicating an authenticity degreeof an identified damaged region) corresponding to the damaged regions.Fast R-CNN, YOLO, Mask-FCN, and the like are all deep neural networksthat include convolutional layers and that can be used in thisembodiment. The deep neural network used in the embodiments, incombination with a region proposal layer and the CNN layer, can detectthe damaged portion in the video image and confirm the region of thedamaged portion in the video image. In some embodiments, the CNN partmay use a mature network structure that achieves a good effect in objectrecognition. In a ResNet network, parameters of the model may beobtained through mini-batch gradient descent training by using markeddata.

When the photographer performs video recording by using the client, alocation region of the damaged portion identified by the server may bedisplayed on the client in real time, so that a user can observe andconfirm the damaged portion. After identifying the damaged portion, theserver may track the damaged portion. In addition, in a trackingprocess, as a filming distance and an angle change, a size and alocation of the location region corresponding to the damaged portion inthe video image may also correspondingly change.

In other embodiments, the photographer may interactively change thelocation and the size of the identified damaged portion in the videoimage. For example, the client displays, in real time, the locationregion of the damaged portion detected by the server. If thephotographer considers performing adjustment because the location regionof the damaged portion identified by the server cannot completely coverthe damaged portion observed on the scene, the photographer may adjustthe location and the size of the location region of the damaged portionon the client. For example, the location of the damaged portion isadjusted by moving the location region after the location region isselected by long pressing the damaged portion, or the size of thedamaged portion is adjusted by stretching a frame of the location regionof the damaged portion. After the photographer adjusts and changes thelocation region of the damaged portion on the client, new information ofthe damaged portion may be generated, and then the new information ofthe new damaged portion is sent to the server.

In this way, the photographer may conveniently and flexibly adjust thelocation region of the damaged portion in the video image according to astatus of the damaged portion on the scene, to more accurately locatethe damaged portion, so that the server can obtain high-quality lossassessment images more accurately and reliably.

The server receives the captured video data uploaded by the client,detects the video images in the captured video data to identify thedamaged portion in the video images.

S3: The server classifies the video images based on the detected damagedportion to determine candidate image classification sets of the damagedportion.

Vehicle loss assessment often requires different types of image data,for example, images of the entire vehicle at different angles, an imagethat can display a damaged component, and a close-up detailed image of adamaged portion. In the specification, during obtaining the lossassessment images, the video images may be analyzed to determine, forexample, whether a video image is an image of the damaged vehicle,whether a vehicle component is included in the analyzed image, whetherone or more vehicle components are included, or whether the vehiclecomponent is damaged. In some embodiments, loss assessment imagesrequired for vehicle loss assessment may be correspondingly classifiedinto different types, and other images that do not satisfy lossassessment image requirements may be respectively classified intoanother type. In some embodiments, each frame of image of the capturedvideo may be extracted, identified, and classified to form the candidateimage classification sets of the damaged portion.

In some embodiments, the determined candidate image classification setsmay include: S301: A close-up image set including images displaying thedamaged portion and a component image set including images displaying avehicle component to which the damaged portion belongs.

The close-up image set includes one or more close-up images of thedamaged portion. The component image set includes one or more imagesdisplaying a damaged component of the damaged vehicle, and the damagedcomponent has at least one damaged portion. In some embodiments, thephotographer may film the damaged portion of the damaged vehicle fromnear to far (or from far to near) through movement or zooming in/out.The server may identify and process the frame of image (each frame ofimage may be processed, or frames of image of a video segment may beselected and processed) in the captured video, to determineclassification of video images. In some embodiments, the video images ofthe captured video may be classified into the following three types, forexample:

-   -   a: close-up image: a close-up image of the damaged portion that        can clearly display detailed information of the damaged portion;    -   b: component image, including the damaged portion and that can        display a vehicle component at which the damaged portion is        located; and    -   c: image that does not belong to the “a” type or the “b” type.

In some embodiments, an identification algorithm or classificationrequirements and the like of the a-type image may be determinedaccording to requirements of close-up images of the damaged portion.During identification of the a-type images, in some embodiments, ana-type image may be identified based on a size (an area or a regionspan) of a region occupied by the damaged portion in the current videoimage. If the damaged portion occupies a relatively large region in thevideo image (for example, a size of the region is greater than athreshold, for example, a length or a width of the region is greaterthan one quarter of that of the video image), the video image may bedetermined as an a-type image. In other embodiments, if in analyzedframes of image of a same damaged component, an area of a region of thedamaged portion in a current frame of image is greater than that inother analyzed frames of image that includes the damaged portion, thecurrent frame of image may be determined as the a-type image. Forexample, if a ratio of the area of the region of the damaged portion tothe area of the current frame of image is larger than a preset ratio, oramong the top ratios, the current frame of image may be determined asthe a-type image.

Therefore, in some embodiments, a video image may be classified into theclose-up image set when at least one of the following conditions aresatisfied:

S3011: a ratio of an area of the damaged portion to that of a videoimage including the damaged portion is greater than a first presetratio.

S3012: a ratio of a horizontal coordinate span of the damaged portion toa length of a video image including the damaged portion is greater thana second preset ratio, and/or a ratio of a longitudinal coordinate spanof the damaged portion to a height of the video image including thedamaged portion is greater than a third preset ratio.

S3013: the video image is one of the first K video images or is a videoimage in which a ratio of an area of the corresponding damaged portionto that of the video image is greater a fourth preset ratio, from thevideo images of the same damaged portion, after the video images aresorted in descending order of areas of the damaged portion, where K≥1.

In the a-type damage detailed image, the damaged portion generallyoccupies a relatively large region range. Selection of a damaged portiondetailed image can be well controlled by setting the first preset ratioin S3011 to obtain an a-type image that satisfies the processingrequirements. The area of the region of the damaged portion in thea-type image may be obtained through counting pixel points included inregion of the damaged portion.

In other embodiments, S3012, whether the video image is an a-type imageis alternatively determined according to a coordinate span of thedamaged portion relative to the video image. For example, FIG. 4 is aschematic diagram of determining, based on an identified damagedportion, a close-up image according to some embodiments of thespecification. As shown in FIG. 4, the video image has 800*650 pixels,and the damaged vehicle has two relatively long scratches, a horizontalcoordinate span corresponding to each of which is a length of 600pixels, while the vertical coordinate span corresponding to each ofwhich is very narrow. Thus, although an area of the region of thedamaged portion is less than one tenth of that of the video image towhich the damaged portion belongs, the 600-pixel horizontal coordinatespan of the damaged portion occupies three quarters of the 800-pixellength of the entire video image. Therefore, according to the conditionin S3012, the video image may be marked as an a-type image.

In other embodiments, S3013, the area of the damaged portion may be thearea of the region of the damaged portion in S3011, or may be a spanvalue of a length or a height of the damaged portion.

Certainly, the a-type image may alternatively be identified by combiningthe foregoing various conditions. For example, the area of the region ofthe damaged portion occupies the video image at a ratio and the ratio ofarea is greater than the fourth preset ratio or the region area of thedamage is the maximum in the images of the same damaged region. In someembodiments, the a-type images generally include all or some detailedimage information of the damaged portion.

The first preset ratio, the second preset ratio, the third preset ratio,and the fourth preset ratio that are described above may becorrespondingly set according to image identification precision,classification precision, other processing requirements, or the like.For example, a value of the second preset ratio or the third presetratio may be one quarter.

In some embodiments, in identification of the b-type images, vehiclecomponents (such as a front bumper, a left front fender, and a rightrear door) included in the video images and their locations may bedetected by using a constructed vehicle component detection model. Ifthe damaged portion is located on the detected damaged components, thevideo images may be determined as the b-type images. For example, in avideo image P1, if a component region of the detected damaged componentin the P1 includes the identified damaged portion (generally, the areaof the identified component region is greater than the area of thedamaged portion), the component region in the P1 can be deemed as thedamaged component. Alternatively, in a video image P2, if a damagedregion detected in the P2 and the component region detected in the P2overlap, a vehicle component corresponding to the component region inthe P2 can also be deemed as the damaged component, and the video imageis classified as a b-type image.

In some embodiments, the component detection model detects, by using thedeep neural network, the component and a region of the component in theimage. In some embodiments, the component damage identification modelmay be constructed based on a CNN and an RPN and in combination with apooling layer, an FC layer, and the like. For example, in regard to thecomponent recognition model, multiple models and variations thereof,such as Faster R-CNN, YOLO, and Mask-FCN, based on the CNN and the RPN,may be used. The CNN may use any CNN model, such as ResNet, Inception,VGG, or a variation thereof. Generally, a CNN part of the neural networkmay use a mature network structure, for example, a network such asInception or ResNet, that achieves a relatively good effect in objectrecognition. For example, in a ResNet network, input is a picture, andoutput is multiple component regions, corresponding componentclassification, and confidence (where the confidence herein is aparameter indicating an authenticity degree of an recognized vehiclecomponent). Fast R-CNN, YOLO, Mask-FCN, and the like are all deep neuralnetworks that include convolutional layers and that can be used in theembodiments. The deep neural network used in the embodiments, incombination with a region proposal layer and the CNN layer, can detect avehicle component in a to-be-processed image, and confirm a componentregion of the vehicle component in the to-be-processed image. In someembodiments, the CNN part may use a mature network structure thatachieves a good effect in object recognition. In a ResNet network,parameters of the model may be obtained through mini-batch gradientdescent training by using marked data. FIG. 5 is a schematic structuraldiagram of a model for identifying a damaged component in a video imagethat is constructed by using the method according to some embodiments ofthe specification.

In some embodiments, if a video image satisfies both of the determiningconditions of the a-type images and the b-type images, the video imagemay be classified as both an a-type image and a b-type image.

The server may extract the video images from the captured video data,classifies the video images based on the detected damaged portion, anddetermines the candidate image classification sets of the damagedportion.

S4: The server selects a vehicle loss assessment image from thecandidate image classification sets according to a preset screeningcondition.

An image satisfying the preset screening condition may be selected fromthe candidate image classification sets according to a loss assessmentimage type, clarity, and the like. The preset screening condition may becustomized. For example, multiple (for example, five or ten) imageshaving the highest clarity and having different filming angles may berespectively selected from the a-type images and the b-type images asloss assessment images of the identified damaged portion. The imageclarity may be calculated based on the damaged portion and the imageregion in which the detected vehicle component is located, and may beobtained, for example, by using a method such as a spatial domain-basedoperator (such as a Gabor operator) or a frequency domain-based operator(such as fast Fourier transformation). For the a-type images, generally,all regions in the damaged portion may be displayed by a combination ofone or more images, thereby ensuring that comprehensive damaged regioninformation can be obtained.

The methods for obtaining a vehicle loss assessment image provides asolution of generating a video-based vehicle loss assessment image. Aphotographer may perform video recording of a damaged vehicle by usingthe terminal device, and captured video data may be transmitted to aserver of a system. The server analyzes the video data, identifies adamaged portion, and obtains different types of candidate imagesrequired for loss assessment according to the damaged portion. Then, oneor more loss assessment images of the damaged vehicle may be generatedfrom the candidate images. According to the embodiments of thespecification, high-quality loss assessment images satisfying lossassessment processing requirements can be quickly obtained, therebyimproving efficiency of obtaining loss assessment images, and alsoreducing costs of obtaining and processing loss assessment images byinsurance company operators.

In some embodiments of the methods in the specification, a videocaptured on the client is transmitted to the server, and the server maytrack a location of a damaged portion in the video in real timeaccording to the damaged portion. For example, in the foregoingembodiments, because the damaged vehicle is a static object, the mobileterminal is moved as the photographer moves. Thus, a correspondencebetween neighboring frames of image in the captured video may beobtained by using such image algorithms as an optical flow-basedalgorithm, to implement tracking of the damaged portion. If the mobileterminal has sensors such as an accelerometer and a gyroscope, a motiondirection and an angle of the photographer may further be determined bycombining signal data of these sensors, thereby more precisely trackingthe damaged portion. Therefore, in some embodiments, the method forobtaining a vehicle loss assessment image may further include:

S200: The server tracks the damaged portion in the captured video datain real time to determine a region of the damaged portion in the videoimages; and when determining that the damaged portion is out of a videoimage and then re-enters a video image, the server tracks the damagedportion again to determine a region of the damaged portion based onimage feature data of the damaged portion.

The server may extract image feature data of the damaged region, forexample, scale-invariant feature transform (SIFT) feature data. If thedamaged portion re-enters the video image after the damaged portion isout of the video image, the system can locate the damaged portion andcontinue to track the damaged portion. For example, after aphotographing device is restarted after being powered off, or the filmedregion is moved to areas where no damage occurs, the same damagedportion is filmed again.

The location region of the damaged portion identified by the server maybe displayed on the client in real time, to help a user to observe andconfirm the damaged portion. The client and the server maysimultaneously display the identified damaged portion. The server maytrack the identified damaged portion. In addition, as a filming distanceand an angle change, a size and a location of the location regioncorresponding to the damaged portion in the video image may alsocorrespondingly change. In this way, the server may display, in realtime, the damaged portion tracked by the client, to facilitateobservation and use of an operator of the server.

In other embodiments, during real-time tracking performed by the server,the server may send the region of the tracked damaged portion to theclient, so that the client may display the damaged portion synchronouslywith the server in real time, to help the photographer to observe thedamaged portion located and tracked by the server. Therefore, the methodfor obtaining a vehicle loss assessment image may further include:

S210: The server sends the region of the tracked damaged portion to theclient, for the client to display the region of the damaged portion inreal time.

In some embodiments, the photographer may interactively change thelocation and the size of the damaged portion in the video images. Forexample, when the client displays the identified damaged portion, if thephotographer considers performing adjustment because the region of theidentified damaged portion cannot completely cover the damaged portion,the photographer may adjust the location and the size of the region onthe client. For example, the location of the damaged portion is adjustedby moving the region after the region is selected by long pressing thedamaged portion, or the size of the damaged portion is adjusted bystretching a frame of the region of the damaged portion. After thephotographer adjusts and changes the region of the damaged portion onthe client, new information of the damaged portion may be generated, andthen the new information of the damaged portion is sent to the server.In addition, the server may synchronously update the information of thedamaged portion based on the changed information on the client. Theserver may identify and process subsequent video images according to theupdated information of the damaged portion. Alternatively, a new damagedportion may be indicated by the photographer on the client, and theclient receives the information of the new damaged portion and sends theinformation to the server for processing. In some embodiments, themethod for obtaining a vehicle loss assessment image may furtherinclude:

S220: The server receives new information of the damaged portion sent bythe client, where the new information of the damaged portion isdetermined when the client changes the region of the damaged portionbased on a received interactive instruction; and correspondingly,classifies the video images based on the new information of the damagedportion.

In this way, the photographer may conveniently and flexibly adjust theregion of the damaged portion in the video images according to a statusof the damaged portion on the scene, to more accurately locate thedamaged portion, so that the server obtains high-quality loss assessmentimages.

In some embodiments, when filming a close-up of the damaged portion, thephotographer may continuously film the damaged portion from differentangles. The server may calculate a filming angle of each frame of imageaccording to the tracking of the damaged portion, to select a group ofvideo images at different angles as loss assessment images of thedamaged portion, so that the loss assessment images can accuratelyreflect a type and degree of the damage. Therefore, selecting a vehicleloss assessment image from the candidate image classification setsaccording to a preset screening condition in the above step S4 mayinclude:

S401: Selecting at least one video image as a loss assessment image ofthe damaged portion from the candidate image classification sets of thedamaged portion respectively according to clarity of the video imagesand filming angles of the damaged portion in the video images.

For example, in some accident scenes, component deformation may beobvious at some angles relative to other angles, or if a damagedcomponent has glare or reflection, the glare or reflection changes witha change of the filming angle, and the like. In this embodiments of thespecification, images at different angles are selected as lossassessment images, thereby greatly reducing interference of thesefactors on loss assessment. In some embodiments, if the client hassensors such as an accelerometer and a gyroscope, the filming angle mayalternatively be obtained by using signals of the sensors or obtainedwith assistance of calculation.

In some embodiments, multiple candidate image classification sets may begenerated. When a loss assessment image is selected, one or more types,such as the foregoing “a” type, “b” type, and “c” type, of the candidateimage classification sets may be applied. For example, the lossassessment image may be selected from an a-type candidate imageclassification set and a b-type candidate image classification set. Ina-type images and b-type images, multiple images (for example, fiveimages of a same component are selected, and ten images of a samedamaged portion are selected) having the highest clarity and differentfilming angles are respectively selected as loss assessment images. Theimage clarity may be calculated based on the damaged portion and theimage region in which the detected vehicle component is located, forexample, by using a method such as a spatial domain-based operator (suchas a Gabor operator) or a frequency domain-based operator (such as fastFourier transformation). Generally, for the a-type image, any region ofthe damaged portion appears in at least one of the selected images.

In some embodiments, if detecting that the damaged vehicle has multipledamaged portions, and the damaged portions are close to each other, theserver may simultaneously track the multiple damaged portions, andanalyze and process each damaged portion, to obtain a corresponding lossassessment image. The server performs the foregoing processing on allthe identified damaged portions, to obtain one or more loss assessmentimages of each damaged portion, and then all the generated lossassessment images may be used as loss assessment images of the entiredamaged vehicle. FIG. 6 is a schematic diagram of a processing scenarioof a method for obtaining a vehicle loss assessment image according tosome embodiments of the specification. As shown in FIG. 6, a distancebetween a damaged portion A and a damaged portion B is relatively short,so that the damaged portion A and the damaged portion B may besimultaneously tracked. However, a damaged portion C is located on theother side of a damaged vehicle and is far away from the damaged portionA and the damaged portion B in a captured video. Therefore, instead oftracking the damaged portion C together with the damaged portion A andthe damaged portion B, the damaged portion C may be filmed alone afterthe damaged portion A and the damaged portion B are filmed.

Accordingly, in some embodiments, in response to detecting that thereare at least two damaged portions in the video image, whether a distancebetween the at least two damaged portions satisfies a set proximitycondition is determined; and in response to determining that thedistance between the at least two damaged portions satisfies the setproximity condition, the at least two damaged portions aresimultaneously tracked, and corresponding loss assessment images arerespectively generated.

The proximity condition may be set according to the quantity of damagedportions, sizes of the damaged portions, distances among the damagedportions, and the like in a same video image.

If detecting that at least one of the close-up image set and thecomponent image set of the damaged portion is empty, or the video imagesin the close-up image set do not cover the entire damaged portion, theserver may generate a video recording prompt message, and then send thevideo recording prompt message to the client corresponding to thecaptured video data.

In the foregoing example, if the server cannot obtain a b-type lossassessment image that can be used to determine a vehicle component inwhich the damaged portion is located, the server may return a videorecording prompt message to the client of the photographer, to promptthe photographer to film multiple neighboring vehicle componentsincluding the damaged portion, so as to obtain one or more b-type lossassessment images. If the server cannot obtain an a-type loss assessmentimage, or all a-type images, alone or in combination, cannot cover theentire region of the damaged portion, the server may return a videorecording prompt message to the photographer, to prompt the photographerto film a close-up of the damaged portion to cover the entire region ofthe damaged portion.

In other embodiments, if the server detects that clarity of a capturedvideo image is insufficient (where the clarity is lower than a presetthreshold or lower than average clarity of a recent recorded videosegment), the server may prompt the photographer to move slowly, therebyensuring captured images' quality. For example, a video recording promptmessage is returned to a mobile terminal APP, to prompt a user to payattention to such factors of filming as focusing and illumination thataffect the clarity. For example, the prompt information “Too fast.Please move slowly to ensure image quality.” is displayed.

In some embodiments, the server may maintain a video segment used forgenerating loss assessment images for subsequent viewing,authentication, and the like. Alternatively, the client may upload orcopy loss assessment images in batches to the server after video imagesare captured.

The method for obtaining a vehicle loss assessment image in theforegoing embodiments provides a solution of generating a video-basedvehicle loss assessment image. The photographer may perform videorecording of a damaged vehicle by using the terminal device, andcaptured video data may be transmitted to the server, and the serveranalyzes the video data, identifies the damaged portion, and obtains,according to the damaged portion, different types of candidate imagesrequired for loss assessment, Then, one or more loss assessment imagesof the damaged vehicle may be generated from the candidate images.According to the embodiments of the specification, high-quality lossassessment images satisfying loss assessment processing requirements canbe quickly obtained, thereby improving efficiency of obtaining lossassessment images, and also reducing costs of obtaining and processingloss assessment images by insurance company operators.

In the foregoing embodiments of obtaining the loss assessment images byusing the recorded video data of the damaged vehicle are described inexamples where the client interacts with the server. Based on theforegoing descriptions, the specification provides a method forobtaining a vehicle loss assessment image that can be applicable to aserver. FIG. 7 is a schematic flowchart of the method for obtaining avehicle loss assessment image according to other embodiments of thespecification. As shown in FIG. 7, the method may include:

S10: Receiving captured video data of a damaged vehicle that is uploadedby a terminal device, detect video images in the captured video data toidentify a damaged portion in the video images.

S11: Classifying the video images based on the detected damaged portionto determine candidate image classification sets of the damaged portion.

S12: Selecting a vehicle loss assessment image from the candidate imageclassification sets according to a preset screening condition.

The terminal device may be the client described in the foregoingembodiments, and may be other terminal devices, for example, a databasesystem, a third-party server, or a flash memory. In some embodiments,after receiving the video data obtained through filming of the damagedvehicle and uploaded or copied by the client, the server may detect thecaptured video data to identify the damaged portion, and then classifythe video images in the video data according to the identified damagedportion. Further, the vehicle loss assessment image is generated throughscreening. According to the embodiments, high-quality loss assessmentimages satisfying loss assessment processing requirements can be quicklyobtained, thereby improving efficiency of obtaining loss assessmentimages, and also reducing costs of obtaining and processing lossassessment images by insurance company operators.

Vehicle loss assessment often requires different types of image data,for example, images of the entire vehicle at different angles, imagesthat can display a damaged component, and close-up detailed image of adamaged portion. In some embodiments, required loss assessment imagesmay be correspondingly classified into different types. For example, thedetermined candidate image classification sets may include: a close-upimage set displaying the damaged portion and a component image setdisplaying a vehicle component to which the damaged portion belongs.

Generally, the video images in the candidate image classification sets,for example, the foregoing a-type close-up images, the b-type componentimages, and the c-type images that do not belong to the “a” type or the“b” type, include at least one damaged portion.

In some embodiments of the method for obtaining a vehicle lossassessment image, a video image may be classified into the close-upimage set when at least one of the following conditions are satisfied: aratio of an area of the damaged portion to that of a video imageincluding the damaged portion is greater than a first preset ratio; aratio of a horizontal coordinate span of the damaged portion to a lengthof a video image including the damaged portion is greater than a secondpreset ratio, and/or a ratio of a longitudinal coordinate span of thedamaged portion to a height of the video image including the damagedportion is greater than a third preset ratio; and the video image is oneof the first K video images or is a video image in which a ratio of anarea of the corresponding damaged portion to that of the video image isgreater a fourth preset ratio, from the video images of the same damagedportion, after the video images are sorted in descending order of areasof the damaged portion, where K≥1.

In some embodiments, an identification algorithm or classificationrequirement and the like of the a-type image may be determined accordingto requirements of a damaged portion close-up image for loss assessmentprocessing. During identification of the a-type image, in someembodiments, an a-type image may be identified based on a size (an areaor a region span) of a region occupied by the damaged portion in thecurrent video image. If the damaged portion occupies a relatively largeregion in the video image (for example, a size of the region is greaterthan a threshold, for example, a length or a width of the region isgreater than one quarter of that of the video image), the video imagemay be determined as an a-type image. In other embodiments, if inanalyzed frames of image of a same damaged component, an area of aregion of the damaged portion in a current frame of image is greaterthan that in other analyzed frames of image that includes the damagedportion, the current frame of image may be determined as the a-typeimage. For example, if a ratio of the area of the region of the damagedportion to the area of the current frame of image is larger than apreset ratio, or among the top ratios, the current frame of image may bedetermined as the a-type image.

In other embodiments of the method for obtaining a vehicle lossassessment image, the method may further include: if it is detected thatat least one of the close-up image set and the component image set ofthe damaged portion is empty, or the video images in the close-up imageset do not cover the entire damaged portion, generating a videorecording prompt message; and sending the video recording prompt messageto the terminal device. The terminal device may be the foregoing client,for example, a mobile phone, that interacts with the server.

In other embodiments of the method for obtaining a vehicle lossassessment image, the method may further include: tracking the damagedportion in the captured video data in real time to determine a region ofthe damaged portion in the video images; and when determining that thedamaged portion is out of a video image and then re-enters a videoimage, tracking the damaged portion again to determine a region of thedamaged portion based on image feature data of the damaged portion. Insome embodiments, the region of the damaged portion that is located andtracked again may be displayed on the server.

In other embodiments of the method for obtaining a vehicle lossassessment image, the method may further include: sending information ofthe region of the tracked damaged portion to the terminal device for theterminal device to display the region of the damaged portion in realtime.

A photographer may display the identified damaged portion on the clientin real time, to help a user to observe and confirm the damaged portion.In this way, the photographer may conveniently and flexibly adjust thelocation region of the damaged portion in the video image according to astatus of the damaged portion on the scene, to more accurately locatethe damaged portion, so that the server obtains high-quality lossassessment images.

In another implementation, the photographer may interactively change thelocation and the size of the damaged portion in the video images. Afterthe photographer adjusts and changes the location region of theidentified damaged portion on the client, new information of the damagedportion may be generated, and then the new information of the damagedportion is sent to the server. In addition, the server may synchronouslyupdate the new damaged portion changed on the client. The server mayidentify and process a subsequent video image according to the newdamaged portion. Therefore, in some embodiments of the method forobtaining a vehicle loss assessment image, the method may furtherinclude: receiving new information of the damaged portion sent by theclient, where the new information of the damaged portion is determinedwhen the client changes the region of the damaged portion based on areceived interactive instruction; and correspondingly, classifying thevideo images based on the new information of the damaged portion.

In this way, the photographer may conveniently and flexibly adjust theregion of the damaged portion in the video image according to a statusof the damaged portion on the scene, to more accurately locate thedamaged portion, so that the server obtains high-quality loss assessmentimages.

When filming a close-up of the damaged portion, the photographer maycontinuously film the damaged portion from different angles. The servermay calculate a filming angle of each frame of image according to thetracking of the damaged portion, to select a group of video images atdifferent angles as loss assessment images of the damaged portion, sothat the loss assessment images can accurately reflect a type and degreeof the damage.

Therefore, selecting a vehicle loss assessment image from the candidateimage classification sets according to a preset screening condition mayinclude: selecting at least one video image as a loss assessment imageof the damaged portion from the candidate image classification sets ofthe damaged portion respectively according to clarity of the videoimages and filming angles of the damaged portion in the video images.

If identifying that the damaged vehicle has multiple damaged portions,and the damaged portions are close to each other, the server maysimultaneously track the multiple damaged portions, to generate lossassessment images of each damaged portion. The server performs theforegoing processing on all the damaged portions specified by thephotographer, to obtain the loss assessment image of each damagedportion, and then all the generated loss assessment images may be usedas loss assessment images of the entire damaged vehicle. Therefore, insome embodiments of the method for obtaining a vehicle loss assessmentimage, if it is detected that there are at least two damaged portions inthe video image, whether a distance between the at least two damagedportions satisfies a specified proximity condition is determined; and inresponse to determining that the distance between the at least twodamaged portions satisfies the set proximity condition, the at least twodamaged portions are simultaneously tracked, and corresponding lossassessment images are respectively generated.

The proximity condition may be set according to a quantity of damagedportions, sizes of the damaged portions, distances between the damagedportions, and the like in a same video image.

Based on the embodiments of obtaining the loss assessment image by usingthe captured video data of the damaged vehicle, described in exampleswhere the client interacts with the server, the specification furtherprovides a method for obtaining a vehicle loss assessment image that canbe applicable to a client. FIG. 8 is a schematic flowchart of the methodfor obtaining a vehicle loss assessment image according to still otherembodiments of the specification. As shown in FIG. 8, the method mayinclude:

S20: Performing video recording of a damaged vehicle to obtain videodata.

S21: Sending the captured video data to a processing terminal.

S22: Receiving information of a region that is obtained throughreal-time tracking of a damaged portion and that is returned by theprocessing terminal, and displaying the tracked region, where thedamaged portion is identified through detecting one or more video imagesin the captured video data by the processing terminal.

The processing terminal includes a terminal device that processes thecaptured video data and generates loss assessment images of the damagedvehicle based on the identified damaged portion. For example, theprocessing terminal may be a remote server for performing lossassessment image processing.

In some embodiments, determined candidate image classification sets mayinclude: a close-up image set displaying the damaged portion and acomponent image set displaying a vehicle component to which the damagedportion belongs, for example, the foregoing a-type image and the b-typeimage. If the server cannot obtain a b-type loss assessment image thatcan be used to determine the vehicle component in which the damagedportion is located, the server may return a video recording promptmessage to the client of a photographer, to prompt the photographer tofilm multiple neighboring vehicle components including the damagedportion, so as to obtain one or more b-type loss assessment images. If asystem cannot obtain an a-type loss assessment image, or all a-typeimage, alone or in combination, cannot cover the entire damaged portion,the system may also send a video recording prompt message to thephotographer, to prompt the photographer to film a close-up image of thedamaged portion to cover the entire damaged portion.

Therefore, in some embodiments, the method may further include: S23:receiving and displaying a video recording prompt message sent by theprocessing terminal, where the video recording prompt message isgenerated when the processing terminal detects that at least one of aclose-up image set and a component image set of the damaged portion isempty, or when the one or more video images in the close-up image set donot cover the entire damaged portion.

As described above, in other embodiments, the client may display, inreal time, the region of the damaged portion that is tracked by theserver, and a location and size of the region may be interactivelychanged on the client. Therefore, in other embodiments of the method,the method may further include: S24: determining new information of adamaged portion after changing the region of the damaged portion basedon a received interactive instruction; and send the new information ofthe damaged portion to the processing terminal, for the processingterminal to classify the video images in the video data based on the newinformation of the damaged portion.

According to the methods for obtaining a vehicle loss assessment imageprovided in the foregoing embodiments, a photographer may perform videorecording of a damaged vehicle by using the terminal device, andcaptured video data may be transmitted to the server of a system, andthen the server analyzes the video data, identifies a damaged portion,and obtains, according to the damaged portion, different types ofcandidate images required for loss assessment. Then, a loss assessmentimage of the damaged vehicle may be generated from the candidate images.According to the embodiments, high-quality loss assessment imagessatisfying loss assessment processing requirements can be quicklyobtained, thereby improving efficiency of obtaining loss assessmentimages, and also reducing costs of obtaining and processing lossassessment images by insurance company operators.

In the foregoing embodiments, the methods of obtaining the lossassessment image by using the captured video data of the damaged vehicleare described in examples from a perspective that the client interactswith the server, a perspective of the client, and a perspective of theserver, respectively. In other embodiments, when (or after) a vehiclevideo is filmed on the client, the client may analyze and process thecaptured video to generate loss assessment images. FIG. 9 is a schematicflowchart of the method for obtaining a vehicle loss assessment imageaccording to yet other embodiments of the specification. As shown inFIG. 9, the method includes:

S30: Receiving captured video data of a damaged vehicle.

S31: Detecting video images in the captured video data to identify adamaged portion in the video images.

S32: Classifying the video images based on the detected damaged portionto determine candidate image classification sets of the damaged portion.

S33: Selecting a vehicle loss assessment image from the candidate imageclassification sets according to a preset screening condition.

In some embodiments, a terminal device may include application modulesdeployed in the client. Generally, the terminal device may be ageneral-purpose or dedicated device having a video recording functionand an image processing capability, for example, a client such as amobile phone or a tablet computer. A photographer may perform videorecording of the damaged vehicle by using the client, and the clientanalyzes the captured video data and identifies the damaged portion, togenerate the loss assessment images.

In some embodiments, a server may be further included. The server isconfigured to receive the loss assessment images generated by theclient. The client may transmit the generated loss assessment images tothe server in real time or asynchronously. Therefore, in otherembodiments of the method, the method may further include: S3301:transmitting the loss assessment images to a server in real time; orS3302: asynchronously transmitting the loss assessment images to aserver.

FIG. 10 is a schematic flowchart of the method for obtaining a vehicleloss assessment image according to further other embodiments of thespecification. As shown in FIG. 10, the client may immediately uploadthe generated loss assessment image to the remote server, or may uploador copy loss assessment images in batches to the remote serverafterwards.

Based on the descriptions of the foregoing embodiments where the servergenerates the loss assessment images, locates and tracks the damagedportion, the method for generating a loss assessment image on the clientmay further include other embodiments. For example, a video recordingprompt message is displayed on a photographing terminal after beinggenerated. Other embodiments describe loss assessment image typeclassification and identification, classification manners, and damagedportion identifying, positioning, and tracking. Descriptions of relatedembodiments may be referenced to for details, which are not repeatedherein.

According to the methods for obtaining a vehicle loss assessment imageprovided in the specification, the client may generate a loss assessmentimage based on a captured video of a damaged vehicle. A photographer mayperform video recording of the damaged vehicle by using the client, tocapture video data; and then the client analyzes the captured video dataand identifies a damaged portion, to obtain candidate images ofdifferent types required for loss assessment. Further, the lossassessment images of the damaged vehicle may be generated from thecandidate images. According to the embodiments of the specification,video recording can be performed on the client, and high-quality lossassessment images satisfying loss assessment processing requirements canbe quickly obtained, thereby improving efficiency of obtaining lossassessment images, and also reducing costs of obtaining and processingloss assessment images by insurance company operators.

Based on the foregoing methods for obtaining a vehicle loss assessmentimage, the specification further provides an apparatus for obtaining avehicle loss assessment image. The apparatus may include an apparatususing the system (including a distributed system), software(application), module, component, server, client, and the like in themethods in the specification in combination with necessary hardware.Based on a same innovative concept, an apparatus provided in thespecification is described in the following embodiments. Embodiments ofthe methods and the apparatus are similar. Therefore, for embodiments ofthe apparatus in the specification, refer to the embodiments of theforegoing methods, and repetitions are not described. The followingterms “unit” or “module” may refer to a combination of software and/orhardware having a predetermined function. Although the apparatusesdescribed in the following embodiments are implemented by usingsoftware, embodiments of the apparatus implemented by using hardware, ora combination of software and hardware are also possible and conceived.

FIG. 11 is a schematic structural diagram of modules of an apparatus forobtaining a vehicle loss assessment image according to some embodimentsof the specification. As shown in FIG. 11, the apparatus may include: adata receiving module 101, configured to receive captured video data ofa damaged vehicle that is uploaded by a terminal device; a damagedportion identification module 102, configured to: detect video images inthe captured video data to identify a damaged portion in the videoimages; a classification module 103, configured to: classify the videoimages based on the detected damaged portion, and determine candidateimage classification sets of the damaged portion; and a screening module104, configured to select a vehicle loss assessment image from thecandidate image classification sets according to a preset screeningcondition.

The foregoing apparatus may be applicable to a server, to implementanalyzing and processing of the captured video data uploaded by theclient to obtain the loss assessment image. This application furtherprovides an apparatus for obtaining a vehicle loss assessment image thatcan be applicable to a client. FIG. 12 is a schematic structural diagramof modules of another apparatus for obtaining a vehicle loss assessmentimage according to some embodiments of the specification. In someembodiments, the apparatus may include: a photographing module 201,configured to: perform video recording of a damaged vehicle to obtainvideo data; a communications module 202, configured to send the capturedvideo data to a processing terminal; and a tracking and displayingmodule 203, configured to: receive information of a region of a damagedportion that is obtained through real-time tracking of the damagedportion and that is returned by the processing terminal, and display theregion, where the damaged portion is identified through detecting one ormore video images in the captured video data by the processing terminal.

In some embodiments, the tracking and displaying module 203 may be adisplay unit including a display screen. A photographer may indicate thedamaged portion in the display screen, and may also display the trackedlocation region of the damaged portion on the display screen.

The method for obtaining a vehicle loss assessment image provided in thespecification may be implemented by a processor's executingcorresponding program instructions in a computer. An apparatus forobtaining a vehicle loss assessment image apparatus provided in thespecification may include a processor and a memory configured to storeinstructions executable by the processor, and the processor executes theinstructions to implement: receiving captured video data of a damagedvehicle; detecting video images in the captured video data to identify adamaged portion in the video image; classifying the video images basedon the detected damaged portion to determine candidate imageclassification sets of the damaged portion; and selecting a vehicle lossassessment image from the candidate image classification sets accordingto a preset screening condition.

The apparatus may be a server. The server receives the captured videodata uploaded by a client, and then perform analysis and processing,including damaged portion identification, type classification, imageselection, and the like, to obtain the vehicle loss assessment images.In some embodiments, the apparatus may alternatively be a client. Afterperforming video recording of the damaged vehicle, the client performsanalysis and processing to obtain the vehicle loss assessment images.Therefore, in some embodiments of the apparatus in the specification,the captured video data of the damaged vehicle may be uploaded by aterminal device; or obtained, through video recording of the damagedvehicle, by the apparatus for obtaining a vehicle loss assessment image.

Further, in the embodiments where the apparatus obtains the capturedvideo data and performs analysis and processing to obtain the lossassessment images, the apparatus may further send the obtained lossassessment images to the server, and the server stores the lossassessment images or performs further loss assessment processing.Therefore, in other embodiments of the apparatus, if the captured videodata of the damaged vehicle is obtained through the video recording, bythe apparatus for obtaining a vehicle loss assessment image, theprocessor executes the instructions to further implement: transmittingthe loss assessment image to a processing terminal in real time; orasynchronously transmitting the loss assessment image to a processingterminal.

Based on the descriptions that, for example, the loss assessment imageis generated, and the damaged portion is located and tracked, of theforegoing method or apparatus embodiments, the apparatus for generatinga loss assessment image on the client in the specification may furtherinclude other embodiments. For example, a video recording prompt messageis displayed on a photographing terminal after being generated. Otherembodiments describe loss assessment image type classification andidentification, classification manners, and damaged portion positioningand tracking. Descriptions of related embodiments may be referenced tofor details, which are not repeated herein.

A photographer may perform video recording of the damaged vehicle byusing the apparatus for obtaining a vehicle loss assessment imageprovided in the specification, to capture video data; and then analyzesthe captured video data to obtain candidate images of different typesrequired for loss assessment. Further, the loss assessment images of thedamaged vehicle may be generated from the candidate images. According tothe embodiments of the specification, video recording can be performedon the client, and high-quality loss assessment images satisfying lossassessment processing requirements can be quickly obtained, therebyimproving efficiency of obtaining loss assessment images, and alsoreducing costs of obtaining and processing loss assessment images byinsurance company operators.

The methods or the apparatuses in the foregoing embodiments of thespecification can implement task logic and record the task logic on astorage medium by using a computer program, and the storage medium maybe readable and executable by a computer to achieve effects of thesolutions described in the embodiments of the specification. Therefore,the specification further provides a computer-readable storage medium,the computer-readable storage medium stores computer instructions, andwhen the instructions are executed, the following steps can beimplemented: receiving captured video data of a damaged vehicle;detecting video images in the captured video data to identify a damagedportion in the video images; classifying the video images based on thedetected damaged portion to determine candidate image classificationsets of the damaged portion; and selecting a vehicle loss assessmentimage from the candidate image classification sets according to a presetscreening condition.

The specification further provides another computer-readable storagemedium, the computer-readable storage medium stores computerinstructions, and when the instructions are executed, the followingsteps are implemented: performing video recording of a damaged vehicleto obtain captured video data; sending the captured video data to aprocessing terminal; and receiving information of a region of a damagedportion that is obtained through real-time tracking of the damagedportion and that is returned by the processing terminal, and displayingthe region, where the damaged portion is identified through detectingone or more video images in the captured video data by the processingterminal.

The computer-readable storage medium may include a physical apparatusconfigured to store information. Generally, the information is stored byusing a medium in an electrical, magnetic, optical, or another formafter the information is digitized. The computer-readable storage mediumdescribed in the embodiments may include: an apparatus that storesinformation by using electrical energy, for example, various types ofmemories such as a RAM and a ROM; an apparatus that stores informationby using magnetic energy, for example, a hard disk, a floppy disk, amagnetic tape, a magnetic core memory, a bubble memory, or a USB flashdrive; and an apparatus that optically stores information, for example,a CD or a DVD. Certainly, there may be readable storage medium in otherforms, for example, a quantum memory, or a graphene memory.

The foregoing apparatuses, methods or computer-readable storage mediamay be applicable to a server for obtaining a vehicle loss assessmentimage, to obtain vehicle loss assessment images based on vehicle imagevideos. The server may be an independent server, or a system clusterincluding multiple application servers, or a server in a distributedsystem. In some embodiments, the server may include a processor and amemory configured to store instructions executable by the processor, andthe processor executes the instructions to implement: receiving capturedvideo data of a damaged vehicle that is uploaded by a terminal device;detecting video images in the captured video data to identify a damagedportion in the video image; classifying the video images based on thedetected damaged portion to determine candidate image classificationsets of the damaged portion; and selecting a vehicle loss assessmentimage from the candidate image classification sets according to a presetscreening condition.

The foregoing apparatuses, methods or computer-readable storage mediamay be applicable to a terminal device for obtaining a vehicle lossassessment image, to obtain vehicle loss assessment images based onvehicle image videos. The terminal device may be implemented as aserver, or may be implemented as a client that performs video recordingof the damaged vehicle on the scene. FIG. 13 is a schematic structuraldiagram of a terminal device according to some embodiments of thespecification. In an embodiment, the terminal device may include aprocessor and a memory configured to store instructions executable bythe processor, and the processor executes the instructions to implement:obtaining video data captured through video recording of a damagedvehicle; detecting video images in the captured video data to identify adamaged portion in the video images; classifying the video images basedon the detected damaged portion to determine candidate imageclassification sets of the damaged portion; and selecting a vehicle lossassessment image from the candidate image classification sets accordingto a preset screening condition.

The obtained captured video data may be data information uploaded afterthe terminal device obtains the captured video data, or may be videodata captured by directly performing video recording of the damagedvehicle by the terminal device.

Further, if the terminal device is the client that performs videorecording, the processor executes the instructions to further implement:transmitting the loss assessment image to a server in real time; orasynchronously transmitting the loss assessment image to a server.

A photographer may perform video recording of the damaged vehicle byusing the terminal device for obtaining a vehicle loss assessment imageprovided in the specification, to capture video data, and then theclient analyzes the captured video data and identifies the damagedportion, to obtain candidate images of different types required for lossassessment. Further, the loss assessment images of the damaged vehiclemay be generated from the candidate images. According to the embodimentsof the specification, video recording can be performed on the client,and high-quality loss assessment images satisfying loss assessmentprocessing requirements can be quickly obtained, thereby improvingefficiency of obtaining loss assessment images, and also reducing costsof obtaining and processing loss assessment images by insurance companyoperators.

Although the specification describes data model construction, dataobtaining, interaction, calculation, determining, and the like, in thedamaged region tracking manners, detecting the damaged portion and thevehicle component by using the CNN and the RPN, and the damagedportion-based image identification and classification, the specificationis not limited to satisfying the industry communication standards,standard data models, computer processing and storage rules, or theembodiments described in the specification. Some industry standards orembodiments that have been slightly modified in a customized manner orbased on the embodiments described in the specification can also achievethe same, equivalent, or similar effects as those of the foregoingembodiments, or predictable effects after the changes. Embodimentsobtained after applying these modifications or changes to the dataobtaining, storage, determining, and processing manners can still belongto the scope of embodiments of the specification.

In the 1990s, improvements of a technology can be clearly classified ashardware improvements (for example, improvements to a circuit structuresuch as a diode, a transistor, a switch, etc.) or software improvements(improvements to a method procedure). However, with the development oftechnologies, improvements of many method procedures can be consideredas direct improvements of hardware circuit structures. Designers almostalways program an improved method procedure to a hardware circuit, toobtain a corresponding hardware circuit structure. Therefore, it cannotbe said that the improvement of a method procedure cannot be implementedby using a hardware entity module. For example, a programmable logicdevice (PLD) such as a field programmable gate array (FPGA) is a type ofintegrated circuit whose logic function is determined by a user'sprogramming the device. The designers perform voluntary programming to“integrate” a digital system into a single PLD without requiring a chipmanufacturer to design and prepare a dedicated integrated circuit chip.In addition, instead of making an integrated circuit chip manually, theprogramming is mostly implemented by using “logic compiler” software,which is similar to the software compiler used to write programs.Original code before being compiled is also written in a specificprogramming language, which is referred to as Hardware DescriptionLanguage (HDL). There are many types of HDLs, such as Advanced BooleanExpression Language (ABEL), Altera Hardware Description Language (AHDL),Confluence, Cornell University Programming Language (CUPL), HDCal, JavaHardware Description Language (JHDL), Lava, Lola, MyHDL, PALASM, RubyHardware Description Language (RHDL), etc. Currently, Very-High-SpeedIntegrated Circuit Hardware Description Language (VHDL) and Verilog aremost commonly used. A person skilled in the art should also understandthat as long as a method procedure is logically programmed and thenprogrammed to an integrated circuit by using the foregoing hardwaredescription languages, a hardware circuit that implements the logicalmethod procedure can be easily obtained.

The controller can be implemented in any suitable manners, for example,the controller can take the form of, for example, a microprocessor orprocessor and a computer-readable medium storing computer-readableprogram code (for example, software or firmware) executable by theprocessor, a logic gate, a switch, an application-specific integratedcircuit (ASIC), a programmable logic controller and an embeddedmicrocontroller. Examples of the controller include, but are not limitedto, the following microcontrollers: ARC 625D, Atmel AT91SAM, MicrochipPIC18F26K20 and Silicone Labs C8051F320. The memory controller can alsobe implemented as part of the memory control logic. A person skilled inthe art will also appreciate that, in addition to implementing thecontroller in the form of pure computer-readable program code, it isalso possible to implement the controller in the form of a logic gate,switch, application-specific integrated circuit, programmable logiccontroller, and embedded microcontroller and other forms to achieve thesame function. Such a controller can be considered as a hardwarecomponent and apparatuses included therein for implementing variousfunctions can also be considered as structures inside the hardwarecomponent. Alternatively, apparatuses configured to implement variousfunctions can be considered as both software modules implementing themethod and structures inside the hardware component.

The systems, the apparatuses, the modules or the units described in theforegoing embodiments can be implemented by a computer chip or an entityor implemented by a product having a particular function. A typicaldevice is a computer. The computer may be, for example, a personalcomputer, a laptop computer, an in-vehicle man-machine interactivedevice, a cellular phone, a camera phone, a smartphone, a personaldigital assistant, a media player, a navigation device, an email device,a game console, a tablet computer, a wearable device, or a combinationof any of the devices.

Although the specification provides method operation steps described inthe embodiments or flowcharts, more or fewer operational stepsoperational steps may be included based on conventional means ornon-creative means. The order of the steps listed in the embodiment ismerely one of multiple step execution orders, and does not indicate theonly execution order. When an actual apparatus or terminal product isexecuted, sequential execution or parallel execution may be performedaccording to the method orders shown in the embodiments or the accompanydrawings (for example, in a parallel processor or multi-threadprocessing environment, and even a distributed data processingenvironment). The term “include,” “comprise,” or their any othervariants is intended to cover a non-exclusive inclusion, so that aprocess, a method, a product, or a device that includes a series ofelements not only includes such elements, but also includes otherelements not expressly listed, or further includes elements inherent tosuch a process, method, product, or device. Unless otherwise indicated,other same or equivalent elements existing in the process, the method,the product, or the device that includes the elements are not excluded.

For ease of description, when a foregoing apparatus is described, theapparatus is divided into units according to functions describedrespectively. Certainly, in the embodiments of the specification, thefunctions of the modules may be implemented in a same piece of ormultiple pieces of software and/or hardware, or modules implementing asame function may be implemented by using a combination of multiplesubmodules or subunits. For example, the foregoing described apparatusembodiments are merely examples. For example, the unit division ismerely logical function division and may be other divisions in otherembodiments. For example, multiple units or components may be combinedor integrated into another system, or some features may be ignored ornot performed. In addition, the displayed or discussed mutual couplingsor direct couplings or communication connections may be implemented byusing some interfaces. The indirect couplings or communicationconnections between the apparatuses or units may be implemented inelectronic, mechanical, or other forms.

A person skilled in the art will also appreciate that, in addition toimplementing the controller in the form of pure computer-readableprogram code, it is also possible to implement the controller in theform of a logic gate, switch, application-specific integrated circuit,programmable logic controller, and embedded microcontroller and otherforms to achieve the same function. Such a controller can be consideredas a hardware component and apparatuses included therein forimplementing various functions can also be considered as structuresinside the hardware component. Alternatively, apparatuses configured toimplement various functions can be considered as both software modulesimplementing the method and structures inside the hardware component.

The application is described with reference to the flowcharts and/orblock diagrams of the methods, the devices (systems), and the computerprogram products according to the embodiments of the specification. Itshould be understood that computer program instructions may be used forimplementing each process and/or each block in the flowcharts and/or theblock diagrams and a combination of a process and/or a block in theflowcharts and/or the block diagrams. These computer programinstructions may be provided to a general-purpose computer, a dedicatedcomputer, an embedded processor, or a processor of another programmabledata processing device to generate a machine, so that the instructionsexecuted by the computer or the processor of the other programmable dataprocessing device generate an apparatus for implementing a specificfunction in one or more processes in the flowcharts and/or in one ormore blocks in the block diagrams.

These computer program instructions may alternatively be stored in acomputer-readable memory that can instruct the computer or the otherprogrammable data processing device to work in a manner, so that theinstructions stored in the computer-readable memory generate an artifactthat includes an instruction apparatus. The instruction apparatusimplements a specific function in one or more processes in theflowcharts and/or in one or more blocks in the block diagrams.

These computer program instructions may alternatively be loaded onto thecomputer or the other programmable data processing device, so that aseries of operations and steps are performed on the computer or theother programmable device, thereby generating computer-implementedprocessing. Therefore, the instructions executed on the computer or theother programmable device provide steps for implementing a function inone or more processes in the flowcharts and/or in one or more blocks inthe block diagrams.

In a typical configuration, a computing device includes one or moreprocessors (CPU), an input/output interface, a network interface, and amemory.

The memory may include, among computer-readable media, a volatile memorysuch as a random access memory (RAM) and/or a non-volatile memory suchas a read-only memory (ROM) or a flash memory (flash RAM). The memory isan example of the computer-readable medium.

The computer-readable medium includes non-volatile, volatile, movable,and unmovable media that may implement information storage by using anymethod or technology. Information may be a computer-readableinstruction, a data structure, a program module, or other data. Examplesof computer storage media include but are not limited to a phase changememory (PRAM), a static random access memory (SRAM), a dynamic randomaccess memory (DRAM), other type of random access memory (RAM), aread-only memory (ROM), an electrically erasable programmable read-onlymemory (EEPROM), a flash memory or other memory technology, a compactdisc read-only memory (CD-ROM), a digital versatile disc (DVD) or otheroptical storage, a cassette magnetic tape, tape and disk storage orother magnetic storage device or any other non-transmission media thatmay be configured to store information that a computing device canaccess. Based on the description in the specification, thecomputer-readable medium does not include transitory computer-readablemedia (transitory media), such as a modulated data signal and a carrier.

A person skilled in the art should understand that the embodiments ofthe specification may be provided as a method, a system, or a computerprogram product. Therefore, the specification may use a form of hardwareonly, software only, or a combination of software and hardware.Moreover, the specification may use a form of a computer program productthat is implemented on one or more computer-usable storage media(including but not limited to a disk memory, a CD-ROM, an opticalmemory, and the like) that include computer-usable program code.

The specification can be described in the general context of computerexecutable instructions executed by a computer, for example, a programmodule. Generally, the program module includes a routine, a program, anobject, a component, a data structure, and the like for executing aparticular task or implementing a particular abstract data type. Thespecification can also be practiced in a distributed computingenvironment in which tasks are performed by remote processing devicesthat are connected through a communication network. In a distributedcomputing environment, the program module may be located in both localand remote computer storage media including storage devices.

The embodiments of the specification are described in a progressivemanner. For same or similar parts in the embodiments, refer to theseembodiments. Each embodiment focuses on a difference from otherembodiments. Especially, a system embodiment is basically similar to amethod embodiment, and therefore is described briefly; for relatedparts, refer to partial descriptions in the method embodiment. In thedescriptions of this specification, descriptions of a reference termsuch as “an embodiment,” “some embodiments,” “an example,” “a specificexample,” or “some examples” means that a feature, structure, material,or characteristic that is described with reference to the embodiment orthe example is included in at least one embodiment or example of thespecification. In this specification, schematic descriptions of theforegoing terms do not necessarily directed at a same embodiment orexample. Besides, the described feature, the structure, the material, orthe characteristic may be combined in a proper manner in any one or moreembodiments or examples. In addition, if not mutually contradictory, aperson skilled in the art can combine or group different embodiments orexamples that are described in this specification as well as features ofthe different embodiments or examples.

The foregoing descriptions are merely embodiments of the specification,and are not intended to limit the specification. For a person skilled inthe art, various modifications and changes may be made to thespecification. Any modifications, equivalent replacements, improvements,and the like made within the spirit and principle of the specificationshall fall within the scope of the claims of the specification.

What is claimed is:
 1. A computer-implemented method for obtaining avehicle loss assessment image comprising: receiving video data of adamaged vehicle; detecting one or more video images in the video data toidentify a damaged portion in the one or more video images; classifyingthe one or more video images into one or more candidate imageclassification sets of the damaged portion based on the identifieddamaged portion; and selecting a vehicle loss assessment image from theone or more candidate image classification sets according to a screeningcondition.
 2. The computer-implemented method for obtaining a vehicleloss assessment image according to claim 1, wherein the one or moredetermined candidate image classification sets comprises: a close-upimage set including one or more video images displaying the damagedportion and a component image set including one or more video imagesdisplaying a vehicle component to which the damaged portion belongs. 3.The computer-implemented method for obtaining a vehicle loss assessmentimage according to claim 2, wherein classifying one or more video imagesinto the close-up image set comprises: in response to determining that aratio of an area of the damaged portion to that of a video imageincluding the damaged portion is greater than a first preset ratio,classifying the video image into the close-up image set.
 4. Thecomputer-implemented method for obtaining a vehicle loss assessmentimage according to claim 2, wherein classifying one or more video imagesinto the close-up image set comprises: in response to determining that aratio of a horizontal coordinate span of the damaged portion to a lengthof a video image including the damaged portion is greater than a secondpreset ratio, and/or a ratio of a longitudinal coordinate span of thedamaged portion to a height of the video image including the damagedportion is greater than a third preset ratio, classifying the videoimage into the close-up image set.
 5. The computer-implemented methodfor obtaining a vehicle loss assessment image according to claim 2,wherein classifying one or more video images into the close-up image setcomprises: sorting video images including the damaged portion in adescending order of areas of the same damaged portion in the videoimages; and selecting, from the sorted video images, first one or morevideo images or one or more video images in each of which a ratio of anarea of the corresponding damaged portion to that of the video image isgreater than a fourth preset ratio.
 6. The computer-implemented methodfor obtaining a vehicle loss assessment image according to claim 2,further comprising: in response to detecting that at least one of theclose-up image set and the component image set of the damaged portion isempty, or the one or more video images in the close-up image set do notcover the entire damaged portion, generating a video recording promptmessage; and sending the video recording prompt message to the terminaldevice.
 7. The computer-implemented method for obtaining a vehicle lossassessment image according to claim 1, further comprising: tracking thedamaged portion in the video data in real time to determine a region ofthe damaged portion in the video images; and in response to the damagedportion being out of a video image and subsequently re-entering a videoimage, tracking the damaged portion again to determine a new region ofthe damaged portion in the video image based on image feature data ofthe damaged portion.
 8. The computer-implemented method for obtaining avehicle loss assessment image according to claim 7, further comprising:sending information of the region of the tracked damaged portion to aterminal device for the terminal device to display the region of thedamaged portion in real time.
 9. The computer-implemented method forobtaining a vehicle loss assessment image according to claim 8, furthercomprising: receiving new information of the damaged portion, whereinthe new information of the damaged portion is determined in response tothe terminal device's changing the region of the damaged portion basedon a received interactive instruction; and classifying the video imagesbased on the new information of the damaged portion.
 10. Thecomputer-implemented method for obtaining a vehicle loss assessmentimage according to claim 1, wherein selecting a vehicle loss assessmentimage from the one or more candidate image classification sets accordingto a screening condition comprises: selecting at least one video imageas a loss assessment image of the damaged portion from the one or morecandidate image classification sets according to clarity of the videoimages and filming angles of the damaged portion in the video images.11. The computer-implemented method for obtaining a vehicle lossassessment image according to claim 1, further comprising: in responseto detecting that there are at least two damaged portions in the one ormore video images, determining whether a distance between the at leasttwo damaged portions satisfies a proximity condition; and in response todetermining that the distance between the at least two damaged portionssatisfies the proximity condition, simultaneously tracking the at leasttwo damaged portions, and obtaining loss assessment images of the atleast two damaged portions respectively.
 12. An apparatus for obtaininga vehicle loss assessment image, comprising one or more processors andone or more non-transitory computer-readable memories coupled to the oneor more processors and configured with instructions executable by theone or more processors to cause the apparatus to perform operationscomprising: receiving video data of a damaged vehicle; detecting one ormore video images in the video data to identify a damaged portion in theone or more video images; classifying the one or more video images intoone or more candidate image classification sets of the damaged portionbased on the identified damaged portion; and selecting a vehicle lossassessment image from the one or more candidate image classificationsets according to a screening condition.
 13. The apparatus for obtaininga vehicle loss assessment image according to claim 12, wherein the oneor more determined candidate image classification sets comprises: aclose-up image set including one or more video images displaying thedamaged portion and a component image set including one or more videoimages displaying a vehicle component to which the damaged portionbelongs.
 14. The apparatus for obtaining a vehicle loss assessment imageaccording to claim 13, wherein classifying one or more video images intothe close-up image set comprises: in response to determining that aratio of an area of the damaged portion to that of a video imageincluding the damaged portion is greater than a first preset ratio,classifying the video image into the close-up image set.
 15. Theapparatus for obtaining a vehicle loss assessment image according toclaim 13, wherein classifying one or more video images into the close-upimage set comprises: in response to determining that a ratio of ahorizontal coordinate span of the damaged portion to a length of a videoimage including the damaged portion is greater than a second presetratio, and/or a ratio of a longitudinal coordinate span of the damagedportion to a height of the video image including the damaged portion isgreater than a third preset ratio, classifying the video image into theclose-up image set.
 16. The apparatus for obtaining a vehicle lossassessment image according to claim 13, wherein classifying one or morevideo images into the close-up image set comprises: sorting video imagesincluding the damaged portion in a descending order of areas of the samedamaged portion in the video images; and selecting, from the sortedvideo images, first one or more video images or one or more video imagesin each of which a ratio of an area of the corresponding damaged portionto that of the video image is greater than a fourth preset ratio. 17.The apparatus for obtaining a vehicle loss assessment image according toclaim 13, wherein the operations further comprise: in response todetecting that at least one of the close-up image set and the componentimage set of the damaged portion is empty, or the one or more videoimages in the close-up image set do not cover the entire damagedportion, generating a video recording prompt message; and sending thevideo recording prompt message to the terminal device.
 18. The apparatusfor obtaining a vehicle loss assessment image according to claim 12,wherein the operations further comprise: tracking the damaged portion inthe video data in real time to determine a region of the damaged portionin the video images; and in response to the damaged portion being out ofa video image and subsequently re-entering a video image, tracking thedamaged portion again to determine a new region of the damaged portionin the video image based on image feature data of the damaged portion.19. The apparatus for obtaining a vehicle loss assessment imageaccording to claim 18, wherein the operations further comprise: sendinginformation of the region of the tracked damaged portion to a terminaldevice for the terminal device to display the region of the damagedportion in real time.
 20. A non-transitory computer-readable storagemedium configured with instructions executable by one or more processorsto cause the one or more processors to perform operations comprising:receiving video data of a damaged vehicle; detecting one or more videoimages in the video data to identify a damaged portion in the one ormore video images; classifying the one or more video images into one ormore candidate image classification sets of the damaged portion based onthe identified damaged portion; and selecting a vehicle loss assessmentimage from the one or more candidate image classification sets accordingto a screening condition.